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Network-based algorithms for target identification and drug repositioning from genetic associations

基于网络的算法，用于根据遗传关联进行目标识别和药物重新定位

基本信息

批准号：
10462769
负责人：
Casey S Greene
金额：
$ 54.94万
依托单位：
UNIVERSITY OF COLORADO DENVER
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10462769
关键词：
3-Dimensional Academia Address Affect Algorithmic Software Algorithms American Bioinformatics Biological Biological Assay Biological Products Cardiovascular system Catalogs Chromosome Mapping Chromosome Structures Complex Computing Methodologies Data Databases Diabetes Mellitus Diagnosis Disease Drug Industry Drug Targeting Electronic Health Record Funding Genes Genetic Genetic Predisposition to Disease Genetic study Genome Genomics Genotype Goals Grant Health system Human Hypertension Individual Lead Link Low-Density Lipoproteins Measurement Methods Mission Molecular National Human Genome Research Institute Network-based Noise Outcome Pathogenesis Pathway Analysis Pathway interactions Pharmaceutical Preparations Phenocopy Phenotype Play Precision Medicine Initiative Proteins Public Health Research Research Personnel Role Specificity Structure Testing Therapeutic Time Tissues Translating United States National Institutes of Health Untranslated RNA Variant Work algorithm development alternative treatment base biobank causal variant data resource design diverse data drug candidate drug development drug discovery drug metabolism effective therapy exome sequencing experimental study gene network genetic association genetic variant genome wide association study genomic data human disease innovation insight novel therapeutics phenome phenotypic data prevent primary outcome trait translational impact

项目摘要

In the field of genetics, genome-wide association studies of common variants (GWAS) and exome sequencing- based analyses are a common strategy to elucidate the relationship between genetic variants and a specific phenotype. While these approaches have strengths, they also have significant limitations such as their inability to identify complex biological interactions that lead to genetic predispositions, their inability to integrate distinct but related phenotypes, and their inability to separate genetic variants effects by tissue. If a phenotype is manifest only as a result of the complex interplay of multiple factors, it can be impossible to successfully isolate individual parts by investigating genotype-phenotype associations for only one outcome trait or disease alone. To affect a disease, drugs need to act on the right target and in the right tissue. Bioinformatics approaches that integrate multiple key layers of information to reveal effective drugs will address a critical unmet need because it is expected that a complex interplay of factors forms the basis for most human phenotypes and diseases. The overall objective of this proposal is the development of algorithms that integrate gene and phenome-wide association results with chromosome structure data and functional relationship networks to identify genes that give rise to complex phenotypes and drugs that modify them. These algorithms will provide a new and unique means to study the genetic etiology of complex traits and outcomes, increasing the interpretability of and ultimately the insights generated from high throughput association testing. The proposal's rationale is that robust tissue-specific methods will open the door for geneticists, researchers with biorepositories, and those with access to other extensive phenotyping data to effectively reposition drugs and identify new targets. Complementary algorithms to address distinct aspects of this challenge are proposed as specific aims: (AIM 1) Development of algorithms that integrate exome sequencing results with biological networks to identify genes and pathways associated with phenotypes in specific tissues; (AIM 2) Development of algorithms that integrate 3D genome structure with robust associations via biological networks to identify genes underlying phenotypes in specific tissues; (AIM 3) Development of algorithms that identify drugs that specifically alter regions of gene- gene networks associated with a complex phenotype. Methods will be applied to phenome-wide analysis of the Geisinger Health System MyCode® biorepository and a subset of candidates will be validated via molecular assays. The outcomes of this grant, namely algorithms for tissue-specific network analysis of genes and drugs, are expected to generate positive translational impact because such algorithms enable researchers to translate existing data resources into causal genes and effective drugs.

在遗传学领域，常见变异的全基因组关联研究（GWAS）和外显子组测序